Why Testify? Witnesses' Motivations for Giving Evidence in a War Crimes Tribunal in Sierra Leone
Bibliographic record
Abstract
Although witnesses are indispensable to the operation and success of war crimes courts, little is known about their motivations for agreeing to testify. This article advances existing knowledge by drawing on findings from interviews conducted with 200 witnesses after they gave evidence in the Special Court for Sierra Leone. Participants were asked to describe their reasons for testifying. Content analysis was used to examine the variety and frequency of responses. Overall, 18 conceptually distinct motivations were mentioned, with most witnesses reporting multiple motivations. The response given most frequently was ‘to denounce wrongs committed against me during the war,’ followed by ‘to contribute to public knowledge about the war.’ Desires for retributive justice (e.g., accountability, punishment), and to fulfill a moral duty to other victims, were each mentioned by approximately one in four witnesses. Other key motivations included establishing the truth and narrating their stories. Motivations differed by gender, age, victimization status, side (prosecution versus defense) and trial. The results support the idea that witnesses value the opportunity to publicly denounce atrocities committed against themselves and others. The findings point to both congruities and incongruities between the aims of witnesses and the goals of war crimes courts. Further, the findings suggest that there may be two broad, overarching aspects of the decision to testify: those that are primarily geared toward helping oneself and those that are primarily geared toward helping others. Pragmatically, the findings can enhance efforts to support witnesses in preparing for and completing their testimonies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".